Using an ensemble of artificial neural networks to convert snow depth to snow water equivalent over Canada

Canada's water cycle is driven mainly by snowmelt. Snow water equivalent (SWE) is the snow-related variable that is most commonly used in hydrology, as it expresses the total quantity of water (solid and liquid) stored in the snowpack. Measurements of SWE are, however, expensive and not continuously accessible in real time. This motivates a search for alternative ways of estimating SWE from measurements that are more widely available and continuous over time. SWE can be calculated by multiplying snow depth with the bulk density of the snowpack. Regression models proposed in the literature first estimate snow density and then calculate SWE. More recently, a novel approach to this problem has been developed and is based on an ensemble of multilayer perceptrons (MLPs). Although this approach compared favourably with existing regression models, snow density values at the lower and higher ends of the range remained inaccurate. Here, we improve upon this recent method for determining SWE from snow depth. We show the general applicability of the method through the use of a large data set of 234 779 snow depth-density-SWE records from 2878 non-uniformly distributed sites across Canada. These data cover almost four decades of snowfall. First, it is shown that the direct estimation of SWE produces better results than the estimation of snow density followed by the calculation of SWE. Second, optimizing MLP parameters separately for each snow climate class further improves the accuracy of SWE estimates. A comparison with commonly used regression models reveals that the ensemble of MLPs proposed here leads to noticeably more accurate estimates of SWE. This study thus shows that delving deeper into artificial neural network theory helps improve SWE estimation and that using a greater number of MLP parameters could lead to even further improvements.